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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 65872

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Dr. Davide Luciano De Luca

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Guest Editor

Department of Informatics, Modelling, Electronics and System Engineering, University of Calabria, 87036 Arcavacata di Rende, CS, Italy
Interests: stochastic processes; rainfall fields modelling; mathematics; statistics; GIS; early warning systems

Dr. Andrea Petroselli

E-Mail Website1 Website2 Website3
Guest Editor

Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy
Interests: rainfall-runoff modeling; flood prone area estimation; surface hydrology; GIS terrain analysis for hydrogeomorphic applications; hydrological processes monitoring and modelling
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Special Issue Information

Dear Colleagues,

Rainfall is the main input for all hydrological models such as, for example, rainfall-runoff models and forecasting of landslides triggered by precipitation. Consequently, the need of improving the modelling of rainfall fields constitutes a key aspect for i) realizing efficient early warning systems and ii) carrying out analyses of future scenarios related to occurrences and magnitudes for all the induced phenomena.

The aim of this Special Issue is to provide a collection of innovative contributions for rainfall modelling, focusing on hydrological scales and on a context of climate changes. In particular, the following topics are of interest:

Statistical analysis of rainfall extremes, mainly focusing on Intensity-Duration-Frequency (IDF) curves and evaluation of Rainfall Thresholds;
Temporal and spatial rainfall distribution;
Transient Stochastic Rainfall Generators, suitable for obtaining long and perturbed time series into a context of climate changes;
Models for rainfall nowcasting at hydrological scales, which can also couple several data sources (from rain gauge networks, weather radar, outputs from Limited Area Models) into a Bayesian framework;
Rainfall downscaling

Dr. Davide Luciano De Luca
Assoc. Prof. Andrea Petroselli
Guest Editor

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Keywords

IDF curves
Return period
Rainfall thresholds
Temporal and spatial rainfall distribution
Stochastic Rainfall Generators
Bayesian framework
Rainfall nowcasting
Rainfall downscaling

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Published Papers (12 papers)

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Editorial

Jump to: Research

2 pages, 172 KiB

Open AccessEditorial

Advances in Modelling of Rainfall Fields

by Davide Luciano De Luca and Andrea Petroselli

Hydrology 2022, 9(8), 142; https://doi.org/10.3390/hydrology9080142 - 10 Aug 2022

Viewed by 1613

Abstract

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well [...] Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

Research

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18 pages, 7380 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Deep Convective Systems in the Colombian Andean Region

by Nicolás Velásquez

Hydrology 2022, 9(7), 119; https://doi.org/10.3390/hydrology9070119 - 28 Jun 2022

Cited by 5 | Viewed by 2161

Abstract

In tropical regions, deep convective systems are associated with extreme rainfall storms that usually detonate flash floods and landslides in the Andean Colombian region. Several studies have used satellite data to address the structure and formation of tropical convective storms. However, there is a local gap in the characterization, which is essential for a better understanding of flash floods and preparedness, filling a gap in a region with scarce information regarding extreme events. In this work, we assess the deep convective storms in a mountainous region of Colombia using meteorological radar observations between 2014 and 2017. We start by identifying convective and stratiform formations. We refine the convective identification by classifying convective systems into enveloped (contained in a stratiform system) and unenveloped (not contained). Then, we analyze the systems’ temporal and spatial distributions and contrast them with the watersheds’ features. According to our results, unenveloped convective systems have higher reflectivity and hence higher rainfall intensities. Moreover, they also have a well-defined spatial and temporal distribution and are likely to occur in watersheds with elevation gradients of around 2000

m

and an aspect contrary to the wind direction. Our assessment of the convective storms is of significant value for the hydrologic community working on flash floods. Moreover, the spatiotemporal description is highly relevant for stakeholders and future local analysis. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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20 pages, 3967 KiB

Open AccessArticle

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

by Amrit Bhusal, Utsav Parajuli, Sushmita Regmi and Ajay Kalra

Hydrology 2022, 9(7), 117; https://doi.org/10.3390/hydrology9070117 - 27 Jun 2022

Cited by 34 | Viewed by 7544

Abstract

Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC-HMS) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research’s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. Furthermore, we also performed a hydraulic simulation in Hydrological Engineering Center—Geospatial River Analysis System (HEC-RAS) using the input discharge obtained from the Random Forest model. The reliability of the Random Forest model and the HEC-HMS model was evaluated using different statistical indexes. The coefficient of determination (R²), standard deviation ratio (RSR), and normalized root mean square error (NRMSE) were 0.94, 0.23, and 0.17 for the training data and 0.72, 0.56, and 0.26 for the testing data, respectively, for the Random Forest model. Similarly, the R², RSR, and NRMSE were 0.99, 0.16, and 0.06 for the calibration period and 0.96, 0.35, and 0.10 for the validation period, respectively, for the HEC-HMS model. The Random Forest model slightly underestimated peak discharge values, whereas the HEC-HMS model slightly overestimated the peak discharge value. Statistical index values illustrated the good performance of the Random Forest and HEC-HMS models, which revealed the suitability of both models for hydrology analysis. In addition, the flood depth generated by HEC-RAS using the Random Forest predicted discharge underestimated the flood depth during the peak flooding event. This result proves that HEC-HMS could compensate Random Forest for the peak discharge and flood depth during extreme events. In conclusion, the integrated machine learning and physical-based model can provide more confidence in rainfall-runoff and flood depth prediction. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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17 pages, 3081 KiB

Open AccessArticle

Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia

by Kindie Engdaw Tadesse, Assefa M. Melesse, Adane Abebe, Haileyesus Belay Lakew and Paolo Paron

Hydrology 2022, 9(5), 66; https://doi.org/10.3390/hydrology9050066 - 19 Apr 2022

Cited by 12 | Viewed by 4160

Abstract

This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, PERSIANNN_CDR_D, and TAMSAT_D) estimated with observed values from 1983 to 2014. Performance evaluation of global precipitation products and their downscaled versions is important for accurate use of those measured values in water resource management, climate, and hydrological applications, particularly in the data-sparse Wabi Shebelle River Basin, Ethiopia. Categorical and quantitative evaluation index techniques were applied. The spatial downscaled global precipitation products outperformed raw spatial resolution estimates in all statistical indicators. TAMSAT-D had acceptable performance ratings in terms of RMSE, CC, and scatter plots (R²). CHIRPSv2 showed the least performance at a daily timestep. Performance of global precipitation products and their downscaled versions increased when daily data were aggregated to the monthly data. CHIRPS-D performed better than other products with a minimum error value (RMSE) and higher CC at a monthly timestep. On the other hand, PERSIANN_CDR_D showed a relatively good performance with a lower, positive Pbias and higher POD values compared to other products for daily and monthly timescales. For spatial mismatch analysis, the bias and RMSE from reference data (individual rain gauge station vs. the average of all available eight stations) against satellite rainfall estimates (PERSIANN_CDR) had a significantly different weight, which could be related to the position of the gauge station to provide the “true” spatial rainfall amount. Overall, TAMSATv3 and CHIRPSv2 and their downscaled version satellite estimates showed good performance at daily and monthly timesteps, respectively. PERSIANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERSIANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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16 pages, 3577 KiB

Open AccessArticle

Estimation of Peak Discharges under Different Rainfall Depth–Duration–Frequency Formulations

by Andrea Gioia, Beatrice Lioi, Vincenzo Totaro, Matteo Gianluca Molfetta, Ciro Apollonio, Tiziana Bisantino and Vito Iacobellis

Hydrology 2021, 8(4), 150; https://doi.org/10.3390/hydrology8040150 - 8 Oct 2021

Cited by 11 | Viewed by 3323

Abstract

One of the main signatures of short duration storms is given by Depth–Duration–Frequency (DDF) curves. In order to provide reliable estimates for small river basins or urban catchments, generally characterized by short concentration times, in this study the performances of different DDF curves proposed in literature are described and compared, in order to provide insights on the selection of the best approach in design practice, with particular reference to short durations. With this aim, 28 monitoring stations with time series of annual maximum rainfall depth characterized by sample size greater than 20 were selected in the Northern part of the Puglia region (South-Eastern Italy). In order to test the effect of the investigated DDF curves in reproducing the design peak discharge corresponding to an observed expected rainfall event, the Soil Conservation (SCS) curve number (CN) approach is exploited, generating peak discharges according to different selected combinations of the main parameters that control the critical rainfall duration. Results confirm the good reliability of the DDF curves with three parameters to adapt on short events both in terms of rainfall depth and in terms of peak discharge and, in particular, for durations up to 30 min, the three-parameter DDF curves always perform better than the two-parameter DDF. Full article

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13 pages, 2339 KiB

Open AccessArticle

Integrative Assessment of Stormwater Infiltration Practices in Rapidly Urbanizing Cities: A Case of Lucknow City, India

by Jheel Bastia, Binaya Kumar Mishra and Pankaj Kumar

Hydrology 2021, 8(2), 93; https://doi.org/10.3390/hydrology8020093 - 12 Jun 2021

Cited by 6 | Viewed by 3603

Abstract

The lack of strategic planning in stormwater management has made rapidly urbanizing cities more vulnerable to urban water issues than in the past. Low infiltration rates, increase in peak river discharge, and recurrence of urban floods and waterlogging are clear signs of unplanned rapid urbanization. As with many other low to middle-income countries, India depends on its conventional and centralized stormwater drains for managing stormwater runoff. However, in the absence of a robust stormwater management policy governed by the state, its impact trickles down to a municipal level and the negative outcome can be clearly observed through a failure of the drainage systems. This study examines the role of onsite and decentralized stormwater infiltration facilities, as successfully adopted by some higher income countries, under physical and social variability in the context of the metropolitan city of Lucknow, India. Considering the 2030 Master Plan of Lucknow city, this study investigated the physical viability of the infiltration facilities. Gridded ModClark rainfall-runoff modeling was carried out in Kukrail river basin, an important drainage basin of Lucknow city. The HEC-HMS model, inside the watershed modeling system (WMS), was used to simulate stormwater runoff for multiple scenarios of land use and rainfall intensities. With onsite infiltration facilities as part of land use measures, the peak discharge reduced in the range of 48% to 59%. Correlation analysis and multiple regression were applied to understand the rainfall-runoff relationship. Furthermore, the stormwater runoff drastically reduced with decentralized infiltration systems. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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32 pages, 20566 KiB

Open AccessFeature PaperArticle

STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series

by Davide Luciano De Luca and Andrea Petroselli

Hydrology 2021, 8(2), 76; https://doi.org/10.3390/hydrology8020076 - 3 May 2021

Cited by 18 | Viewed by 5679

Abstract

The MS Excel file with VBA (Visual Basic for Application) macros named STORAGE (STOchastic RAinfall GEnerator) is introduced herein. STORAGE is a temporal stochastic simulator aiming at generating long and high-resolution rainfall time series, and it is based on the implementation of a Neymann–Scott Rectangular Pulse (NSRP) model. STORAGE is characterized by two innovative aspects. First, its calibration (i.e., the parametric estimation, on the basis of available sample data, in order to better reproduce some rainfall features of interest) is carried out by using data series (annual maxima rainfall, annual and monthly cumulative rainfall, annual number of wet days) which are usually longer than observed high-resolution series (that are mainly adopted in literature for the calibration of other stochastic simulators but are usually very short or absent for many rain gauges). Second, the seasonality is modelled using series of goniometric functions. This approach makes STORAGE strongly parsimonious with respect to the use of monthly or seasonal sets for parameters. Applications for the rain gauge network in the Calabria region (southern Italy) are presented and discussed herein. The results show a good reproduction of the rainfall features which are mainly considered for usual hydrological purposes. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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17 pages, 5696 KiB

Open AccessArticle

Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq

by Ahmed Naseh Ahmed Hamdan, Suhad Almuktar and Miklas Scholz

Hydrology 2021, 8(2), 58; https://doi.org/10.3390/hydrology8020058 - 26 Mar 2021

Cited by 75 | Viewed by 13690

Abstract

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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17 pages, 5782 KiB

Open AccessArticle

Intra-Storm Pattern Recognition through Fuzzy Clustering

by Konstantinos Vantas and Epaminondas Sidiropoulos

Hydrology 2021, 8(2), 57; https://doi.org/10.3390/hydrology8020057 - 25 Mar 2021

Cited by 5 | Viewed by 3320

Abstract

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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22 pages, 3012 KiB

Open AccessArticle

Rainfall Intensity-Duration-Frequency Relationship. Case Study: Depth-Duration Ratio in a Semi-Arid Zone in Mexico

by Ena Gámez-Balmaceda, Alvaro López-Ramos, Luisa Martínez-Acosta, Juan Pablo Medrano-Barboza, John Freddy Remolina López, Georges Seingier, Luis Walter Daesslé and Alvaro Alberto López-Lambraño

Hydrology 2020, 7(4), 78; https://doi.org/10.3390/hydrology7040078 - 15 Oct 2020

Cited by 10 | Viewed by 7602

Abstract

Intensity–Duration–Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration, and return period. They are commonly used in the design, planning and operation of hydrologic, hydraulic, and water resource systems. Considering the intense rainfall presence with flooding occurrences, limited data used to develop IDF curves, and importance to improve the IDF design for the Ensenada City in Baja California, this research study aims to investigate the use and combinations of pluviograph and daily records, to assess rain behavior around the city, and select a suitable method that provides the best results of IDF relationship, consequently updating the IDF relationship for the city for return periods of 10, 25, 50, and 100 years. The IDF relationship is determined through frequency analysis of rainfall observations. Also, annual maximum rainfall intensity for several duration and return periods has been analyzed according to the statistical distribution of Gumbel Extreme Value (GEV). Thus, Chen’s method was evaluated based on the depth-duration ratio (R) from the zone, and the development of the IDF relationship for the rain gauges stations was focused on estimating the most suitable (R) ratio; chosen from testing several methods and analyzing the rain in the region from California and Baja California. The determined values of the rain for one hour and return period of 2 years

(P_{1}^{2})

obtained were compared to the values of some cities in California and Baja California, with a range between 10 and 16.61 mm, and the values of the (R) ratio are in a range between 0.35 and 0.44; this range is close to the (R) ratio of 0.44 for one station in Tijuana, a city 100 km far from Ensenada. The values found here correspond to the rainfall characteristics of the zone; therefore, the method used in this study can be replicated to other semi-arid zones with the same rain characteristics. Finally, it is suggested that these results of the IDF relationship should be incorporated on the Norm of the State of Baja California as the recurrence update requires it upon recommendation. This study is the starting point to other studies that imply the calculation of a peak flow and evaluation of hydraulic structures as an input to help improve flood resilience in the city of Ensenada. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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20 pages, 7641 KiB

Open AccessArticle

Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

by Héctor A. Ballinas-González, Víctor H. Alcocer-Yamanaka, Javier J. Canto-Rios and Roel Simuta-Champo

Hydrology 2020, 7(4), 73; https://doi.org/10.3390/hydrology7040073 - 5 Oct 2020

Cited by 21 | Viewed by 4493

Abstract

Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R²—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km² was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R² > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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21 pages, 6574 KiB

Open AccessArticle

Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal

by Kalpana Hamal, Shankar Sharma, Nitesh Khadka, Binod Baniya, Munawar Ali, Mandira Singh Shrestha, Tianli Xu, Dibas Shrestha and Binod Dawadi

Hydrology 2020, 7(3), 40; https://doi.org/10.3390/hydrology7030040 - 13 Jul 2020

Cited by 42 | Viewed by 5882

Abstract

Precipitation is the most important variable in the climate system and the dominant driver of land surface hydrologic conditions. Rain gauge measurement provides precipitation estimates on the ground surface; however, these measurements are sparse, especially in the high-elevation areas of Nepal. Reanalysis datasets are the potential alternative for precipitation measurement, although it must be evaluated and validated before use. This study evaluates the performance of second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) datasets with the 141-gauge observations from Nepal between 2000 and 2018 on monthly, seasonal, and annual timescales. Different statistical measures based on the Correlation Coefficient (R), Mean Bias (MB), Root-Mean-Square Error (RMSE), and Nash–Sutcliffe efficiency (NSE) were adopted to determine the performance of both MERRA-2 datasets. The results revealed that gauge calibrated (MERRA-C) underestimated, whereas model-only (MERRA-NC) overestimated the observed seasonal cycle of precipitation. However, both datasets were able to reproduce seasonal precipitation cycle with a high correlation (R ≥ 0.95), as revealed by observation. MERRA-C datasets showed a more consistent spatial performance (higher R-value) to the observed datasets than MERRA-NC, while MERRA-NC is more reasonable to estimate precipitation amount (lower MB) across the country. Both MERRA-2 datasets performed better in winter, post-monsoon, and pre-monsoon than in summer monsoon. Moreover, MERRA-NC overestimated the observed precipitation in mid and high-elevation areas, whereas MERRA-C severely underestimated at most of the stations throughout all seasons. Among both datasets, MERRA-C was only able to reproduce the observed elevation dependency pattern. Furthermore, uncertainties in MERRA-2 precipitation products mentioned above are still worthy of attention by data developers and users. Full article

(This article belongs to the Special Issue Advances in Modelling of Rainfall Fields)

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